Mobile devices that acquire images, such as, for example, 3G phones, camcorders, I-Phones®, etc., typically comprise an integrated image sensor (for example, a CMOS image sensor) coupled to a digital video encoder. In various embodiments, an image sensor, such as, for example, a CMOS image sensor, may comprise an array of active pixel sensors (APSs), each comprising a photoelectric converter element, such as, for example, a photodiode or a phototransistor, which may able to convert the received photons into an electric output current. This current can be used for charging a capacitance and generating a difference of potential corresponding to the light signal at input. This analog signal may then be converted into digital form and processed by a processor, such as, for example, a so-called “image sensor pipeline” (ISP). It is thus possible to obtain an output digital image, which may be sent to a digital video encoder to be subjected to a compression operation.
A device is schematically represented in FIG. 1, where the reference number 10 designates the image sensor on which the light signal LS impinges, and the reference number 12 designates the processor designed to produce an output digital image I. The image I is sent to the encoder 14, which generates on its output a corresponding bit stream BS.
The signal generated the sensor 10 (for example, a CMOS sensor) may be affected by two main types of noise: thermal noise and impulsive noise (shot noise). Thermal noise (sometimes also referred to as “Johnson-Nyquist noise”) may be generated by thermal agitation, which may induce a Brownian motion in the electrons that flow in a semiconductor, thus causing a random fluctuation of the electrical potential. Thermal noise can be basically modeled as a white noise, i.e. as a noise that ideally covers the entire frequency spectrum. As is known, the thermal noise may be modeled from the analytical standpoint in the form of a random variable/process with a probability-distribution function of a Gaussian type, the variance of which expresses the power of the thermal noise.
Impulsive noise or shot noise arises when the finite number of the particles that carry energy—as is the case of the electrons that flow through a junction in a semiconductor, or the photons in an optical device—is so low that any change in the number causes a measurable fluctuation in the corresponding signal.
In poor-lighting conditions or when the dimensions of the pixel sensor or APS are particularly small (a situation that is rather common today when the number of megapixels in the image sensors increases constantly), the number of photons arriving on each pixel sensor or APS may be very small so that the photocurrent (i.e. the electric current generated by the photoelectric converter, for example, a phototransistor) is affected by impulsive noise.
Given that impulsive noise is a random fluctuation associated to a discrete event, it may be modeled from the analytical standpoint as a random variable/process with Poisson distribution, defined by a probability function of the type:
      f    ⁡          (              k        ,        λ            )        =                              λ          k                ⁢                  ⅇ                      -            λ                                      k        !              .  
In a Poisson random process, both the mean value and the variance are equal to λ. Poisson noise depends upon the number of incoming photons and is hence correlated to the input signal, whereas this does not occur for Gaussian noise. This implies the fact that different segments in a sequence of digital images can contain different amounts of noise, so that, to be effective, filtering of the Poisson noise may take this phenomenon into account. Whereas, the typical approaches are dedicated to filtering Gaussian noise, relatively little attention has so far been dedicated to the problem of filtering Poisson noise.